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When designing an electric vehicle (EV) traction system, overcoming the issues arising from the variations in the battery voltage due to the state of charge (SoC) is critical, which otherwise can lead to a deterioration of the powertrain energy efficiency and overall drive performance. However, systems are typically documented under fixed voltage and temperature conditions, potentially lacking comprehensive specifications that account for these variations across the entire range of the vehicle operating regions. To tackle this challenge, this paper seeks to adjust an optimal DC-link voltage across the complete range of drive operating conditions by integrating a DC-DC converter into the powertrain, thereby enhancing powertrain efficiency. This involves conducting a comprehensive analysis of power losses in the power electronics of a connected converter-inverter system considering the temperature variations, along with machine losses, accounting for variable DC-link voltages.

Correct simulations of rotating wheels are essential for accurate aerodynamic investigations of passenger vehicles. Therefore, modern automotive wind tunnels are equipped with five-belt moving ground systems with wheel drive units (WDUs) connected to the underfloor balance. The pressure distribution on the exposed areas of the WDU belts results in undesired lift forces being measured which must be considered to obtain accurate lift values for the vehicle. This work investigates the parasitic WDU lift for various configurations of a crossover SUV using numerical simulations that have been correlated to wind tunnel data. Several parameters were considered in the investigation, such as WDU size, WDU placement, tyre variants and vehicle configurations. The results show that the parasitic lift is more sensitive to the width than the length of the WDU. However, the belt length is also important to consider, especially if the wheel cannot be placed centred.

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets.

Bushing elasticity is one of the most important compliance factors that significantly influence driving behavior. The deformations of the bushings change the wheel orientations under external forces. Another important factor of bushing compliance is to provide a comfortable driving experience by isolating the vibrations from road irregularities. However, the driving comfort and driving dynamics are often in conflict and need to be balanced in terms of bushing compliance design. Specifically, lateral force steer and brake force steer are closely related to safety and stability and comprises must be minimized. The sensitivity analysis helps engineers to understand the critical bushing for certain compliance attributes, but optimal balancing is complicated to understand. The combination of individual bushing stiffness must be carefully set to achieve an acceptable level of all the attributes.

In vehicle development, a subjective evaluation of the vehicle’s behavior at high speeds is usually conducted by experienced drivers with the objective of assessing driving stability. To avoid late design changes, it is desirable to predict and resolve perceived instabilities early in the development phase. In this study, a mathematical model is developed from measurements during on-road tests to predict the driver’s ability to identify vehicle instabilities under excitations such as aerodynamic excitations. A vehicle is fitted with add-ons to create aerodynamic excitations and is driven by multiple drivers on a high-speed track. Drivers’ evaluation, responses, cabin motion, and crosswind conditions are recorded. The influence of yaw and roll rates, lateral acceleration, and steering angle at various frequency ranges when predicting the drivers’ evaluation of induced excitation is demonstrated. The drivers’ evaluation of vehicle behavior is influenced by driver-vehicle interactions.

Wind tunnels are an essential tool in vehicle development. To simulate the relative velocity between the vehicle and the ground, wind tunnels are typically equipped with moving ground and boundary layer control systems. For passenger vehicles, wind tunnels with five-belt systems are commonly used as a trade-off between accurate replication of the road conditions and uncertainty of the force measurements. To allow different tyre sizes, the wheel drive units (WDUs) can often be fitted with belts of various widths. Using wider belts, the moving ground simulation area increases at the negative cost of larger parasitic lift forces, caused by the connection between the WDUs and the balance. In this work, a crossover SUV was tested with 280 and 360mm wide belts, capturing forces, surface pressures and flow fields. For further insights, numerical simulations were also used.

The shift toward electrification and limitations in battery electric vehicle technology have led to high demand for hybrid vehicles (HEVs) that utilize a battery and an internal combustion engine (ICE) for propulsion. Although HEVs enable lower fuel consumption and emissions compared to conventional vehicles, they still require combustion of fuels for ICE operation. Thus, emissions from hybrid vehicles are still a major concern. Engine starts are a major source of emissions during any driving event, especially before the three-way catalyst (TWC) reaches its light-off temperature. Since the engine is subjected to multiple starts during most driving events, it is important to mitigate and better understand the impact of these emissions. In this study, experiments were conducted to analyze engine starts in a hybrid powertrain on different experimental setup.

An electrically heated catalyst (EHC) in the three-way catalyst (TWC) aftertreatment system of a gasoline internal combustion engine (ICE) provides cold engine start exhaust pollutant emission reduction potential. The EHC can be started before switching on the ICE, thereby offering the possibility to pre-heat (PRH) the TWC, in the absence of exhaust flow. The EHC can also provide post engine start heat (PSH) when the heat is accompanied by exhaust mass flow over the TWC. A mixed heating strategy (MXH) comprises both PRH and PSH. All three strategies are evaluated under a range of engine start variations using an ICE-exhaust aftertreatment (EATS) simulation framework. It is driven by an engine speed-torque requested trace, with an engine-out emissions model focused on cold-start, engine heating and catalyst heating engine measures and a physics- based EATS with EHC model.

Soot particle size, particle concentration and volume fraction were measured by laser based methods in optically dense, highly turbulent combusting diesel sprays under engine-like conditions. Experiments were done in the Chalmers High Pressure, High Temperature spray rig under isobaric conditions and combusting commercial diesel fuel. Laser Induced Incandescence (LII), Elastic Scattering and Light Extinction were combined quasi-simultaneously to quantify particle characteristics spatially resolved in the middle plane of a combusting spray at two instants after the start of combustion. The influence that fuel injection pressure, gas temperature and gas pressure exert on particle size, particle concentration and volume fraction were studied. Probability density functions of particle size and two-dimensional images of particle diameter, particle concentration and volume fraction concerning instantaneous single-shot cases and average measurements are presented.

Pre-chamber combustion (PCC) has re-emerged in recent last years as a potential solution to help to decarbonize the transport sector with its improved engine efficiency as well as providing lower emissions. Research into the combustion process inside the pre-chamber is still a challenge due to the high pressure and temperatures, the geometrical restrictions, and the short combustion durations. Some fundamental studies in constant volume combustion chambers (CVCC) at low and medium working pressures have shown the complexity of the process and the influence of high pressures on the turbulence levels. In this study, the pre-chamber combustion process was investigated by combustion visualization in an optically-accessible pre-chamber under engine relevant conditions and linked with the jet emergence inside the main chamber. The pre-chamber geometry has a narrow-throat. The total nozzle area is distributed in two six-hole rows of nozzle holes.

We describe experiments and numerical modeling of snow surface contamination on two simplified automotive bluff bodies: The Ahmed body and a wedge. The purpose was twofold: 1) To obtain well defined experimental results of snow contamination on simple geometries; 2) To propose a numerical modeling approach for snow contamination. The experiments were performed in a climatic wind tunnel using a snow cannon at −15 °C and the results show that the snow accumulation depends on the aerodynamics of the studied bluff bodies. Snow accumulates on surfaces in proximity to the aerodynamic wakes of the bodies and characteristic snow patterns are obtained on side surfaces. The numerical modeling approach consisted of an aerodynamic setup coupled with Lagrangian particle tracking. Particles were determined to adhere or rebound depending on an adhesion model combined with a resuspension criterion.

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

Power dense internal combustion engines (ICEs) are interesting candidates for onboard charging devices in different electric powertrain applications where the weight, volume and price of the energy storage components are critical. Single-cylinder naturally aspirated two-stroke spark-ignited (SI) engines are very small and power dense compared to four-stroke SI engines and the installation volume from a single cylinder two-stroke engine can become very interesting in some concepts. During charged conditions, four-stroke engines become more powerful than naturally aspirated two-stroke engines. The performance level of a two-stroke SI engines with a charging system is less well understood since only a limited number of articles have so far been published. However, if charging can be successfully applied to a two-stroke engine, it can become very power dense.

Axial cooling fans are commonly used in electric vehicles to cool batteries with high heating load. One drawback of the cooling fans is the high aeroacoustic noise level resulting from the fan blades and the obstacles facing the airflow. To create a comfortable cabin environment in the vehicle, and to reduce exterior noise emission, a low-noise installation design of the axial fan is required. The purpose of the study is to investigate efficient computational aeroacoustics (CAA) simulation processes to assist the cooling-fan installation design. In this paper we report the current progress of the investigation, where the narrow-band components of the fan noise is focused on. Two methods are used to compute the noise source. In the first method the source is computed from the flow field obtained using the unsteady Reynolds-averaged Navier-Stokes equations (unsteady RANS, or URANS) model.

The potential of 48V Mild Hybrid is promising in meeting the present and future CO2 legislations. There are various system layouts for 48V hybrid system including P0, P1, P2. In this paper, P2 architecture is used to investigate the effects of water injection benefits in a mild hybrid system. Electrification of the conventional powertrain uses the benefits of an electric drive in the low load-low speed region where the conventional SI engine is least efficient and as the load demand increases the IC Engine is used in its more efficient operating region. Engine downsizing and forced induction trend is popular in the hybrid system architecture. However, the engine efficiency is limited by combustion knocking at higher loads thus ignition retard is used to avoid knocking and fuel enrichment becomes must to operate the engine at MBT (Maximum Brake Torque) timing; in turn neutralizing the benefits of fuel savings by electrification.

The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks.

A method for measuring apparent soot particle size and concentration in turbulent combusting diesel jets with elevated and inhomogeneous optical density is presented and discussed. The method is based on the combination of quasi-simultaneous Laser Induced Incandescence (LII), Elastic Scattering (ELS) and Light Extinction (LE) measurements exhibiting a high potential for spatially resolved measurements of carbonaceous particles in flames and residual gases at a given instant. The method evaluates the LII signal by calculating the laser fluence across the flame and compensating for signal trapping, allowing measurements where laser extinction between the flame borders reaches values up to 90%. The method was implemented by measuring particle size and concentration in the middle sagittal axis of optically dense, combusting diesel jets at a certain time after the start of combustion.

The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.

Approximately 25 % of a passenger vehicle’s aerodynamic drag comes directly or indirectly from its wheels, indicating that the rim geometry is highly relevant for increasing the vehicle’s overall energy efficiency. An extensive experimental study is presented where a parametric model of the rim design was developed, and statistical methods were employed to isolate the aerodynamic effects of certain geometric rim parameters. In addition to wind tunnel force measurements, this study employed the flowfield measurement techniques of wake surveys, wheelhouse pressure measurements, and base pressure measurements to investigate and explain the most important parameters’ effects on the flowfield. In addition, a numerical model of the vehicle with various rim geometries was developed and used to further elucidate the effects of certain geometric parameters on the flow field.

48V mild hybrid powertrains are promising technologies for cost-effective compliance with future CO2 emissions standards. Current 48V powertrains with integrated belt starter generators (P0) with downsized engines achieve CO2 emissions of 95 g/km in the NEDC. However, to reach 75 g/km, it may be necessary to combine new 48V powertrain architectures with alternative fuels. Therefore, this paper compares CO2 emissions from different 48V powertrain architectures (P0, P1, P2, P3) with different electric power levels under various driving cycles (NEDC, WLTC, and RTS95). A numerical model of a compact class passenger car with a 48V powertrain was created and experimental fuel consumption maps for engines running on different fuels (gasoline, Diesel, E85, CNG) were used to simulate its CO2 emissions. The simulation results were analysed to determine why specific powertrain combinations were more efficient under certain driving conditions.